Notice bibliographique
Résumé
We thank Schmidt et al.1 for contributing their recent article and letter2 to the literature on heterogeneity of difference and ratio effect measures. The letter wisely emphasizes the relevance of structural limits on the risk difference (RD). Indeed, if RD = −0.30 among women and unexposed men have a risk of 0.20, RD homogeneity is impossible. The simulation results1 for the Q test statistic would be most relevant to the meta-analytic evidence we reviewed.3 With small sample sizes or small fractions of doubly exposed individuals, test size was greater than alpha in the RD homogeneity tests and less than alpha in the tests of odds ratio (OR) homogeneity. This disparity would push the power to detect heterogeneity upward for the RD tests and downward for the OR tests, a result consistent with our conjecture that differential power might be part of the explanation. Two of us (PD, TJV) are engaged in related methodologic work that has mathematically assessed the three-dimensional volume in the four-dimensional space (of the four outcome probabilities under the different exposure combinations) for which homogeneity holds on the risk difference, risk ratio, and OR scales. The respective volumes are 1.33 for the risk difference, 1.76 for the risk ratio, and 2.47 for the OR scale.4 Thus, relatively speaking, there are more values of the outcome probabilities for which OR homogeneity holds than risk ratio homogeneity and more values for which risk ratio homogeneity holds than risk difference homogeneity. However, these are simply mathematical statements over all possible outcome probabilities. The real question is what values the outcome probabilities take empirically with actual exposures and outcomes and how closely these probabilities approximate homogeneity on different scales. We are thus heartened by the agreement on the part of Schmidt et al.1 that the ultimate question is empirical and not theoretical. The suggestion to shun the RD has been made in the belief that it is usually much more heterogeneous than ratio measures such as the OR in empirical research settings. Although there are more heterogeneous possibilities for the RD than for the OR, it would be difficult to defend the assumption that each of those possibilities has the same probability, within or across the many studies that are actually conducted. As noted in our article,3 further evidence is therefore required before concluding that the risk difference is in fact a more heterogeneous measure. Charles Poole Department of Epidemiology Gillings School of Global Public Health University of North Carolina Chapel Hill, NC Ian Shrier Centre for Clinical Epidemiology Lady Davis Institute for Medical Research Jewish General Hospital McGill University Montreal, QC, Canada Peng Ding Departments of Epidemiology and Statistics Harvard University Cambridge, MA Tyler VanderWeele Departments of Epidemiology and Biostatistics Harvard T.H. Chan School of Public Health Harvard University Boston, MA [email protected]
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».